This publication constitutes the refereed complaints of the 14th commercial convention on Advances in information Mining, ICDM 2014, held in St. Petersburg, Russia, in July 2014. The sixteen revised complete papers awarded have been rigorously reviewed and chosen from a variety of submissions. the themes diversity from theoretical points of information mining to purposes of knowledge mining, comparable to in multimedia information, in advertising, in drugs and agriculture and in procedure regulate, and society.

Info Mining: possibilities and demanding situations offers an summary of the state-of-the-art techniques during this new and multidisciplinary box of information mining. the first target of this booklet is to discover the myriad concerns relating to facts mining, particularly concentrating on these parts that discover new methodologies or learn case reviews.

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More and more, people are sensors attractive at once with the cellular net. contributors can now percentage real-time reviews at an exceptional scale. Social Sensing: development trustworthy structures on Unreliable information seems at fresh advances within the rising box of social sensing, emphasizing the main challenge confronted by means of program designers: tips on how to extract trustworthy details from information accumulated from principally unknown and doubtless unreliable assets.

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The classification is performed by nine categories. In general, it should be noted that most often used features that are applied for web page classification are extracted from the page text content. For instance, Dumais and Chen [6] separated concepts of web page text, header information and descriptive information service tag “meta”. They implemented the Support Vector Machine (SVM) method. Lai and Wu [18] used two approaches to obtain necessary features for classification: meaningful term extraction and discriminative term selection.

We experiment with three diﬀerent conﬁgurations for classiﬁcation tasks using all possible pairs of product categories. The conﬁgurations are summarized in Table 3. For each pair, we train the NB classiﬁer using a Training Set and then run the classiﬁer over the corresponding Test Set. In Table 4, we can observe that classiﬁcation results based on all the cleaning methods are dramatically better than the results using the original noisy Web pages. Our method produce a little better result than SST and much better than the other three baselines for Web page classiﬁcation.